def fit(self, fr, **fit_params): res = [] for step in self.steps: res.append(step[1].to_rest(step[0])) res = "[" + ",".join([_quoted(r.replace('"', "'")) for r in res]) + "]" j = H2OConnection.post_json(url_suffix="Assembly", steps=res, frame=fr.frame_id, _rest_version=99) self.id = j["assembly"]["name"] return get_frame(j["result"]["name"])
def model_performance(self, test_data=None): """ Compute the binary classifier model metrics on `test_data` :param test_data: An H2OFrame :return: A H2OBinomialMetrics object; prints model metrics summary """ if not test_data: raise ValueError("Missing`test_data`.") if not isinstance(test_data, H2OFrame): raise ValueError("`test_data` must be of type H2OFrame. Got: " + type(test_data)) fr_key = H2OFrame.send_frame(test_data) url_suffix = "ModelMetrics/models/" + self._key + "/frames/" + fr_key res = H2OConnection.post_json(url_suffix=url_suffix) raw_metrics = res["model_metrics"][0] return H2OBinomialModelMetrics(raw_metrics)